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Rutu Mulkar-mehta - One of the best experts on this subject based on the ideXlab platform.

  • Abductive Reasoning with a Large Knowledge Base for Discourse Processing
    Computing Meaning, 2014
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This chapter presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and produce logical forms. We test the proposed procedure and the resulting knowledge base on the recognizing textual entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

  • IWCS - Abductive reasoning with a large knowledge base for Discourse Processing
    2011
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This paper presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

Marcel Adam Just - One of the best experts on this subject based on the ideXlab platform.

  • Neuroimaging Contributions to the Understanding of Discourse Processes
    Handbook of Psycholinguistics, 2020
    Co-Authors: Robert A. Mason, Marcel Adam Just
    Abstract:

    Publisher Summary This chapter highlights some key neuroimaging studies of Discourse Processing. Some of the components of Discourse Processing revealed by neuroimaging research, like protagonist perspective monitoring, are relatively new to the Discourse Processing theory. At the same time, there is uncertainty about the reality of these networks and about their anatomical location. Moreover, these networks must function in interaction with somewhat lower level comprehension processes that operate at the lexical and sentence level. Dating back to Broca's and Wernicke's findings on brain-damaged patients with specific language deficits in the late 1800s, psychologists have had some idea of the brain's functioning as a language Processing mechanism. Discourse theories become critical in developing the understanding of the cortical Discourse Processing network. In addition, neuroimaging research has led to the development of several new Discourse theories such as the coarse coding theory of right hemisphere Processing, the dynamic recruitment of networks in response to text constraints, a Theory of Mind system responsible for awareness of different perspectives, and the spillover of Processing to other differential specialized networks in response to capacity utilization.

  • brain correlates of Discourse Processing an fmri investigation of irony and conventional metaphor comprehension
    Neuropsychologia, 2006
    Co-Authors: Zohar Eviatar, Marcel Adam Just
    Abstract:

    Higher levels of Discourse Processing evoke patterns of cognition and brain activation that extend beyond the literal comprehension of sentences. We used fMRI to examine brain activation patterns while 16 healthy participants read brief three-sentence stories that concluded with either a literal, metaphoric, or ironic sentence. The fMRI images acquired during the reading of the critical sentence revealed a selective response of the brain to the two types of nonliteral utterances. Metaphoric utterances resulted in significantly higher levels of activation in the left inferior frontal gyrus and in bilateral inferior temporal cortex than the literal and ironic utterances. Ironic statements resulted in significantly higher activation levels than literal statements in the right superior and middle temporal gyri, with metaphoric statements resulting in intermediate levels in these regions. The findings show differential hemispheric sensitivity to these aspects of figurative language, and are relevant to models of the functional cortical architecture of language Processing in connected Discourse.

  • language comprehension sentence and Discourse Processing
    Annual Review of Psychology, 1995
    Co-Authors: Patricia A Carpenter, Akira Miyake, Marcel Adam Just
    Abstract:

    CONTENTS INTRODUCTION .... 91 Marr's Analysis 92 Computational Architectures for Languages ........ 93 SENTENCE COMPREHENSION 94 Lexical Ambiguity 95 Syntactic Processing , 97 Working Memory and Sentence Processing 101 Individual and Population DijJerences 103 Cross-linguistic Studies 106 Conceptual Combination and Integration 107 Discourse COMPREHENSION 108 Establishing Coherence 109 Algorithms in Text Comprehension , 1 12 CONCLUSIONS 115

Ekaterina Ovchinnikova - One of the best experts on this subject based on the ideXlab platform.

  • Abductive Reasoning with a Large Knowledge Base for Discourse Processing
    Computing Meaning, 2014
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This chapter presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and produce logical forms. We test the proposed procedure and the resulting knowledge base on the recognizing textual entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

  • weighted abduction for Discourse Processing based on integer linear programming
    Plan Activity and Intent Recognition#R##N#Theory and Practice, 2014
    Co-Authors: Naoya Inoue, Ekaterina Ovchinnikova, Kentaro Inui, Jerry R. Hobbs
    Abstract:

    This chapter explores the logical framework called weighted abduction as applied to solving Discourse-Processing tasks. Weighted abduction incorporates a cost propagation mechanism allowing us to estimate the likelihood of the obtained abductive proofs. We use a tractable implementation of weighted abduction based on Integer Linear Programming and a large knowledge base generated automatically. We first perform an experiment on plan recognition using the dataset originally developed for Ng and Mooney’s system [39] . Then we apply our Discourse Processing pipeline for predicting whether one text fragment logically entails another one (Recognizing Textual Entailment task). The study we describe is the first attempt to apply tractable inference-based natural language Processing on a large scale.

  • abductive reasoning with a large knowledge base for Discourse Processing
    IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics, 2011
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkarmehta
    Abstract:

    This paper presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

  • IWCS - Abductive reasoning with a large knowledge base for Discourse Processing
    2011
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This paper presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

Jerry R. Hobbs - One of the best experts on this subject based on the ideXlab platform.

  • Abductive Reasoning with a Large Knowledge Base for Discourse Processing
    Computing Meaning, 2014
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This chapter presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and produce logical forms. We test the proposed procedure and the resulting knowledge base on the recognizing textual entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

  • weighted abduction for Discourse Processing based on integer linear programming
    Plan Activity and Intent Recognition#R##N#Theory and Practice, 2014
    Co-Authors: Naoya Inoue, Ekaterina Ovchinnikova, Kentaro Inui, Jerry R. Hobbs
    Abstract:

    This chapter explores the logical framework called weighted abduction as applied to solving Discourse-Processing tasks. Weighted abduction incorporates a cost propagation mechanism allowing us to estimate the likelihood of the obtained abductive proofs. We use a tractable implementation of weighted abduction based on Integer Linear Programming and a large knowledge base generated automatically. We first perform an experiment on plan recognition using the dataset originally developed for Ng and Mooney’s system [39] . Then we apply our Discourse Processing pipeline for predicting whether one text fragment logically entails another one (Recognizing Textual Entailment task). The study we describe is the first attempt to apply tractable inference-based natural language Processing on a large scale.

  • abductive reasoning with a large knowledge base for Discourse Processing
    IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics, 2011
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkarmehta
    Abstract:

    This paper presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

  • IWCS - Abductive reasoning with a large knowledge base for Discourse Processing
    2011
    Co-Authors: Ekaterina Ovchinnikova, Jerry R. Hobbs, M C Mccord, Niloofar Montazeri, Theodore Alexandrov, Rutu Mulkar-mehta
    Abstract:

    This paper presents a Discourse Processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

Nathan Menard - One of the best experts on this subject based on the ideXlab platform.

  • l1 and l2 writers strategic and linguistic knowledge a model of multiple level Discourse Processing
    Language Learning, 1995
    Co-Authors: Karen Whalen, Nathan Menard
    Abstract:

    This study compared the cognitive Processing of 12 anglophone second-year French undergraduate students who were prompted to write an argumentative text in both L1 (English) and L2 (French). The students' speaking aloud protocols and textual drafts provided the basis of collected data. In the first part of the study, the writers' planning, evaluation, and revision strategies were (a) analyzed in terms of the pragmatic., textual, and linguistic manifestations of these processes and (b) compared for differences in Processing behaviors between their L1 and L2 writing. In the second part, we measured linguistic Processing occurrences to analyze their effect on more global Processing behaviors at the pragmatic and textual levels. The linguistic constraints imposed by the writers' knowledge of the second language (French) point toward some significant differences in Discourse level Processing between L1 and L2 writing behaviors. However, the results reveal that the state of the writers' strategic knowledge and capacity for meaningful multiple-level Discourse Processing explains the constraining effects of linguistic Processing on L2 written Discourse production.